Digital Transformation and AI in Energy Systems: Applications, Challenges, and the Path Forward

Abstract

AbstractThe integration of digital technologies like Machine Learning (ML), Artificial Intelligence (AI), and the Internet of Things is transforming energy systems. This digital transformation aims to enhance efficiency, sustainability, and resilience in power generation, transmission, and consumption. A key focus is developing smart grids that leverage real-time data and intelligent algorithms to optimise operations. In response, deep learning and reinforcement learning techniques are being applied to bolster cybersecurity in the energy sector. Deep learning excels at detecting threats by identifying patterns in large datasets. Meanwhile, reinforcement learning can simulate attack scenarios to train adaptive defence strategies. However, cybersecurity threats pose a major risk as energy infrastructure becomes more interconnected. The Colonial Pipeline ransomware attack in 2021 demonstrated the vulnerabilities of critical infrastructure to cyberattacks. Despite great potential, challenges remain regarding model transparency, ethics, and data availability. Overall, realising the promise of AI in the energy sector requires navigating technical complexities and prioritising explainable, trustworthy systems. If implemented thoughtfully, these technologies can catalyse the transition to smarter, more efficient, resilient, and sustainable energy systems.